Connectivism Glossary

Stephen Downes and I have kicked off our third iteration of our open course Connectivism and Connective Knowledge…if interested, you can register here. The course is again being offered as part of the Certificate in Emerging Technologies program at University of Manitoba (i.e. for-credit students). In our course orientation yesterday, someone requested a connectivism glossary. A reasonable question – and one that we replied with our usual “if it’s missing in the course, it’s an opportunity for you to create something”. However, today, via Google Alerts, I came across this glossary from participants in the 2009 course: Connectivism Glossary.

It captures some of the more common terms used in discussing social networked learning. After a quick skim of the items listed, I was left with this sense of “great resource. But we’ve somewhat moved on”. Many of the terms listed were quite helpful in the “early days” of 2004/5 when we were trying to grasp onto language that would help describe the phenomenon that we viewed as important. Terms like “half-life of knowledge”, the “pipe” of content, and “informal learning” I could do away with now. They were transitionary terms that don’t quite seem as relevant now as they did at the time. Essentially, these words were used to try and create a sense of what was happening with knowledge and in society that warranted reflection and reconsideration. They don’t speak directly to what connectivism is, but rather the context that raises the importance of social networked learning.

I’m now more interested in terms that address not only what connectivism is, but the ways in which networks are shaped and impact learning (at the neural, conceptual, and external-social network levels). A few of these include:

Amplification: the connection of one concept or skill set with another complementary concept or skill set that produces a greater impact than each element could produce on its own.

Resonance: when concepts are available to connection with other concepts based on some element of similarity or capacity for connection. For example, a psychologist is in a better position to understand a new theory of motivation than a farmer would be. And a farmer in turn will likely find greater resonance with a new approach to land management than a psychologist would. Resonance is capacity for connections to form based on the attributes of connect-able nodes. Nodes that are too unlike each other will not form a meaningful connection.

Synchronization: nodes/concepts aligning themselves to other agents/concepts (fireflies is a common example).

Information diffusion: how does information flow through a network? Which nodes slow down information flow? Which test the accuracy or trust-ability of information?

Influence: Which concepts or nodes have the capacity to impact others? Which nodes can be trusted? Why? Are single nodes as influential and nodal structures that are in a state of resonance and/or synchronization? (the answer is obviously no). What role do individual nodes play in producing resonance across multiple nodes? Which attributes or actions on the part of nodes contribute most to trust formation and influence generation?

Enacting new domains of knowledge:The virus that causes SARS was discovered through a distributed research network, aided by reasonably simple communication technology. We all possess some levels of knowledge. When that knowledge is connected with the knowledge of other people, we are able to access more complex domains of knowledge. For example, the iPad is the combination of innovations and technological advances that spans decades and centuries. The iPad – and its aesthetic and appeal – can only be realized with the knowledge required in its creation is networked and connected

Connected specialization:In complex systems, individual agents/nodes become increasingly specialized. In order to enact new domains of knowledge (see above), we need to connect specialized nodes. Understanding how and why nodes form and connect may help us to understand why we have an iPad but not a Windows tablet (as promised by Balmer in 2010). Connections have an impact – but we don’t want random connections for connections sake. We need connections that increase the capacity of a network of individuals to create and grow knowledge.

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I need more study on network theory to understand some of the concepts here.

Individual agents/nodes seem restricted to following an entirely predictable path. Rather than making leaps to survive, a node can only scan for familiar options, holding its response to a level that makes it non-viable?

How can something knit from a collection of dumb or at least unimaginative actors result in a dynamic system? How could something like a MOOC emerge from such unpromising origins? I must not be understanding the concept of nodes.

Firstly, like you, I admit to needing more study on network theory. Having said that, it would seem that we both recognise its significance (please correct me if I’m assuming too much).

Rather than being random actors (I’m a farmer interested in land management and you’re a psychologist interested in motivation theory, so:let’s connect and see what happens); we can act in a more precise, defined, targeted manner, based on the concept of resonance (let’s connect: I’m a psychologist interested in motivation theory and so are you).

Does a restriction apply that would restrict my “nodal behaviour” to a predictable seeking out of only those instances of resonance? for me, that would depend strictly on my willingness to both seek out “non-resonant connections” and to accept nonresonant connections, when asked to do so by others.

As I think of my networks, I confess to having large instances of both types, resonant and non-resonant connections. For example, my “Facebook connections”, over 900 people (a small number, really) are 95% made up of teachers of English. We resonate, even beyond the classroom.

On the other hand, my “Linkedin connections” over 1300 people (again, a small number) are largely nonresonant, with an incredible diversity of fields of endeavour represented.

We can see that my networked social learning behaviour could not be described as “predictable”. Now here’s the million dollar question: Is it beneficial, firstly, for me as a teacher of English, and secondly, as a person?

If I try to answer this question, knowing my answer is subjective, rather than a disinterested, objective one. After all, they represent my social network behaviour! It’s like defending yourself at a trial: you are offered the last word before a verdict is rendered.

I can only answer “Yes” to both instances I have illustrated here. The Facebook Teacher of English connections are rich, and have provided me with almost exponential growth in professional learning. Despite Facebook not having the reputation of a powerful professional learning tool, it has worked out tremendously well for me. Yet, I know, this one was almost a given, considering the preponderance of Teachers present.

What about my Linkedin connections? Surely, due to its diversity, should I come in contact with a farmer interested in land management, or a psychologist interested in motivation theory, there will be no value to the connection.

Nonetheless, I answer, a second time, “Yes”. This is a group of valuable connections, despite the fact that I’m a teacher of English, not interested in farming or the psychology of motivation. How so, you would ask?

Well, when the farmers turn their gaze from farming, and the psychologist turns his gaze from psychology, their “humanness” remains. In a holistic sense, we are all more than the sum of our professional interests. As human beings, we share a multitude of common concerns, likes, dislikes, hobbies, pursuits, etc. Mankind: we are genuinely more alike than we are distinct.

It is from this, the shared humanity viewpoint, that enriches my life, again, in a million ways, that I can absolutely testify to its positive beneficial effect on my life.

Scott, I don’t know if I’ve added to your understanding, or caused greater cognitive diddonance for you, but I offer you my personal insights into nodal behaviour. In my opinion, it’s not predictable, and in fact, its unpredictability makes it all the more enjoyable, in my opinion.

Yet I would be remiss, if I didn’t quote George Siemens. In the last session I believe, he was asked: (I paraphrase here) “How do you get rid of a connection that you don’t want? How do you unconnect?”

George answered: “You starve a node. You don’t connect”.

With a lack of interaction on your part, the connection becomes irrelevant, and thusly frees you to seek or devote your time to more beneficial connections.

To conclude, I can only end as I began, namely: “I need more study on network theory to understand it”. Thanks to you Scott, I feel like I’m a bit closer to a useful understanding than I was before.